INTRODUCTION
The popularization of gaming and the rapid advancement in digital technologies have established gaming as a primary leisure activity and a significant sociocultural phenomenon, particularly among adolescents and young adults. While gaming offers various cognitive and social benefits [1], excessive gaming has been increasingly linked to psychological distress ACCESSand behavioral dysfunction, necessitating effective intervention strategies [2]. Gaming disorder (GD) is characterized by persistent and excessive engagement with online and/or offline video or digital games, resulting in impairment in daily functioning [3]. Moreover, GD can encompass broader concepts such as internet gaming disorder (IGD), problematic or pathological video gaming, and excessive video game use [4].
In 2013, the American Psychiatric Association introduced IGD as a proposed diagnostic category in the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) [3]. Subsequently, in 2019, the WHO formally included GD in the eleventh edition of the International Classification of Diseases (ICD-11) [5]. While both classification systems recognize GD as a condition of clinical and public health significance, they differ in their diagnostic criteria, leading to ongoing discussions about the boundaries of pathological gaming [4,6]. Additionally, concerns persist regarding discrepancies in terminology between these classification systems and the potential pathologization of normative gaming behavior [7]. Despite these differences, there is a consensus that excessive gaming can develop into a severe mental health issue, reinforcing the need for effective interventions and standardized treatment strategies [8].
GD is associated with various psychiatric and physical symptoms that significantly impact an individual’s well-being. Key symptoms of GD include insomnia, daytime sleepiness, and chronic fatigue, which are closely associated with increased gaming time [9]. Moreover, individuals with GD often experience intense stress [2,10], which can disrupt family functioning and weaken social relationships [2], and are prone to depression and emotional distress [11]. GD has also been strongly linked to attention-deficit/hyperactivity disorder (ADHD) [1], and affected individuals tend to exhibit significantly lower levels of selfesteem and life satisfaction [12], which severely impact their overall mental health and functionality. Furthermore, compulsive behavioral patterns such as preoccupation with gaming, withdrawal symptoms, and loss of control are hallmarks of the disorder; if these symptoms persist, a diagnosis of GD may be warranted [3,5].
Given the increasing prevalence of GD, there is a growing interest in effective interventions for both its prevention and treatment [8,13]. Individuals with GD can be treated using pharmacological and/or non-pharmacological interventions (NPIs), including psychological and behavioral therapies, with tailored approaches based on the patient’s specific symptoms and needs [12,14]. Pharmacological treatment involves the use of medications, such as bupropion or escitalopram, which are effective in alleviating depression and reducing the severity of GD [15]. However, long-term use of these medications carries risks of side effects and drug dependence, limiting their role as standalone treatments [16,17]. Consequently, NPIs, particularly psychotherapeutic and behavioral interventions, have gained attention as promising alternatives [18].
NPIs have gained attention as treatment options that demonstrate short-term effectiveness without associated side effects. Cognitive behavioral therapy (CBT) is one of the most widely used psychotherapeutic approaches, as it is effective in modifying distorted cognitions and behavioral patterns related to gaming, with numerous studies supporting its efficacy [19]. Family therapy is also a key NPI, enhancing treatment outcomes for adolescents with GD by improving family communication and strengthening support systems [20,21]. Additionally, behavioral interventions such as digital detox programs help individuals with GD reduce their dependency on gaming and reintegrate into the real-life activities [22].
While NPIs have been increasingly recognized for their role in the treatment and prevention of GD, previous systematic reviews have primarily focused on psychological interventions such as CBT and mindfulness [12,23]. However, these reviews lacked a comprehensive evaluation of diverse NPIs [24], including behavioral interventions and educational programs [8,25]. Additionally, they were constrained by small sample sizes and inconsistencies in diagnostic classifications [26,27]. Specifically, many studies have treated GD as a subcategory of internet addiction, often grouping it with other behavioral addictions such as online shopaholics, online gambling addiction, online pornography addiction, and online social networking service (SNS) addiction [28]. This lack of distinction may have affected the accuracy of treatment efficacy assessment. Furthermore, prior reviews did not systematically compare the effectiveness of prevention- and treatment-focused NPIs or analyze intervention effectiveness across different populations [7,29].
Given these gaps, this systematic review and meta-analysis aim to provide a more comprehensive evaluation of NPIs by incorporating a broader range of interventions, conducting detailed subgroup analyses, and assessing their effectiveness across diverse populations. Through this approach, we seek to provide clinically relevant insights and identify optimal intervention strategies for both the prevention and treatment of GD.
METHODS
The systematic review and meta-analysis evaluated the effects of NPIs on the prevention and reduction of GD. This study adhered to the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) statement (Supplementary Tables 1 and 2) [30]. Additionally, this systematic review was pre-registered in the International Prospective Register of Systematic Reviews (PROSPERO; Registration No. CRD42024615740).
Literature search
The literature search was conducted in five electronic databases: MEDLINE (via PubMed), Embase, CENTRAL (via Cochrane Library), PsycINFO, and CINAHL (on Ebsco Host). Additionally, the reference lists were manually searched. The search keywords were categorized into two groups: 1) “gaming disorder” (MeSH Terms) OR “internet gaming disorder” (MeSH Terms) AND 2) “intervention” (Title/Abstract) OR “effect” (Title/Abstract) OR “treatment” OR “therapy” (Title/Abstract). Detailed search strategies for all databases are provided in Supplementary Table 3.
Inclusion and exclusion criteria
Studies in English published up to May 12, 2024, were screened according to the participants, intervention, comparison, outcomes, time, setting, and study design (PICOTS-SD) criteria. Two independent reviewers (Ock and Lee) screened the titles and abstracts. The full texts of potentially eligible studies were obtained for further evaluation. Any disagreement between the two reviewers was resolved through discussion with a third researcher (Kim).
Participants (P): The participants included individuals diagnosed with GD (including IGD), based on diagnostic criteria derived from the Internet Addiction Test (IAT) and the DSM-5, with no age restrictions. This category also encompassed normal gamers as well as those with co-occurring mental disorders alongside GD. In contrast, studies focusing primarily on addiction symptoms or disorders related to online SNS), pornography, shopping, or gambling were excluded. Studies with a minimum sample size of 10 participants per group were included.
Intervention (I): All types of interventions for GD (including IGD), except pharmacological, were included. This encompassed indirect interventions, such as parental strategies to manage their children’s gaming behavior.
Comparison (C): Both inactive controls (no intervention, waitlist, sham, or pseudo-training) and active controls (other types of interventions) were allowed as comparators.
Outcomes (O): The primary outcomes included only GDrelated variables measured using objective or subjective diagnostic criteria, such as the IAT and the DSM-5.
Time (T): No restrictions were applied on the study duration or the follow-up period for trials.
Setting (S): No restrictions were applied regarding the study setting.
Study design (SD): Only randomized controlled trials (RCTs) were included. There were no restrictions on the follow-up period of the trial.
Quality assessment
The risk of bias in the included studies was independently assessed by two reviewers using the Cochrane Risk of Bias Tool for RCTs (RoB2) [31]. The assessment followed the guidelines outlined in the Cochrane Handbook for Systematic Reviews of Interventions [32]. The primary outcomes were analyzed using the intention-to-treat (ITT) approach.
The RoB2 tool addresses five domains of bias: (D1) arising from the randomization process, (D2) due to deviations from intended interventions, (D3) due to missing outcome data, (D4) in the measurement of the outcome, and (D5) in the selection of the reported result. The risk of bias in each domain was rated as “high,” “some concerns,” or “low” and visualized using a traffic light plot. The overall risk of bias was summarized using a bar chart to provide a comprehensive overview of potential biases in the included studies.
Data extraction
Multiple publications using the same dataset were considered as a single study; however, data were extracted for all related publications. To record the basic characteristics of the included studies, a standardized data extraction form was utilized. In cases where a study contained two experimental groups which were independently compared to a control group, each comparison was treated as a separate study [33].
Statistical analysis
A meta-analysis was conducted using Comprehensive Meta-Analysis software (version 3.0; https://www.meta-analysis.com/) to assess the effectiveness of NPIs in reducing the severity of GD. Effect sizes and 95% confidence intervals (CIs) were derived from the pooled data, with a 5% significance level. Effect sizes were calculated using means and standard deviations, with Hedges’s g applied under a random-effects model [34,35].
Heterogeneity was evaluated using the I2 statistic, with thresholds of 25%, 50%, and 75% indicating low, moderate, and high heterogeneity, respectively [36]. In addition, Cochran’s Q test was performed, with a p-value of <0.1 interpreted as evidence of significant heterogeneity [37]. In cases where the correlation coefficient between pre- and post-treatment measurements within the same group was not reported, a conservative estimate of r=0.7 was applied [38]. Publication bias was initially assessed through visual inspection of a comparison-adjusted funnel plot for asymmetry. Further analysis was conducted using the Egger test, where a p-value of <0.05 was considered indicative of potential publication bias [39]. To further evaluate the risk of publication bias, the trim-and-fill method was employed under both fixed-effect and random-effect models [40].
Sensitivity analysis was conducted by sequentially excluding each study to evaluate its influence on the pooled estimate of GD severity. This approach was used to assess the robustness and reliability of the overall findings. To explore heterogeneity across studies and examine variations in effect sizes based on specific factors, subgroup analyses were performed. The following categories were considered: intervention type (psychotherapy, behavioral, or other), intervention goal (prevention or treatment), age group (adolescent or adult), control type (active or inactive), outcome measure (DSM-5 or IAT), geographical region (Asia or Europe), and RoB2 ratings (low, some concerns, or high).
RESULTS
Search results
The preliminary search identified 3,508 potentially relevant articles. After removing 898 duplicates, 2,610 records remained for initial screening. Following a review of titles and abstracts, 65 studies were selected for full-text assessment. Studies were excluded for the following reasons: lack of a relevant population [41-43], absence of reported outcomes (GD severity) [20,44-57], differing study designs [50,58-72], inability to access the full text despite thorough searches [73-82], or being ongoing studies without conclusive results (Supplementary Table 4) [83-86]. Two additional studies were identified through reference checking of related meta-analyses and reviews. Finally, 18 studies were analyzed (Figure 1 and Supplementary Table 5).
Characteristics of the included studies
The characteristics of the included studies are presented in Table 1. All studies were RCTs published in English between 2013 and 2023. A total of 10 studies were conducted in Asia (Korea, China, and Hongkong) [25,87-95] with 778 participants (39.90%); 6, in Europe (Germany, Switzerland, Turkey) [96-101] with 1,102 participants (56.51%); and 2, in other regions (Nigeria, USA) [101,102] with 70 participants (3.59%). None reported conflicts of interest, and all were peer reviewed.
Participants
Of the total participants (n=1,950), 908 were allocated to the NPI group and 1,042 to the control group. The mean number of participants per study was 50.44 (range: 12-167) in the intervention group and 57.89 (range: 12-255) in the control group. The average age of participants ranged from 10.22 to 32.10 years in the NPI group and from 9.97 to 31.50 years in the control group. The proportion of male participants varied widely across studies, ranging from 18.8% to 100%, with 6 studies (k=6, where k denotes the number of studies) exclusively including male participants.
Interventions
The most commonly employed intervention type was psychotherapy (k=10), with most studies utilizing CBT (k=8) (Tables 1 and 2) [87,89,92,93,97,98,100-103]. Four studies implemented behavioral interventions, such as abstinence or response inhibition training using computerized tasks [25,94,95,101]. For prevention, a parental program (k=1) [91] and physical exercise (k=1) [99] were included. Brain stimulation interventions involved tran-scranial direct current stimulation (tDCS, k=2) [88,90]. The intervention durations ranged from 4 hours to 6 months, with a mean duration of 6.37 weeks (Table 1). The number of sessions varied between 1 and 26, with an average of 10.88. The length of each session ranged from 20 minutes to 4 hours, with an average of 78.58 minutes per session.
Comparison group
The comparison groups (control group) were primarily composed of no-treatment (no intervention) and waiting list controls (Table 1). For brain stimulation and certain behavioral interventions, sham interventions or pseudo-training were employed. Four studies utilized treatment-as-usual (TAU) as the control group, which included active comparators such as CBT [87], support groups [102], educational programs [91], family therapy as usual (FTAU) [100], virtual reality therapy [93], and basic counseling [92].
Outcome
All studies utilized self-report questionnaires to assess GD outcomes. The most frequently used tool was IAT, employed in 11 studies to categorize GD as a subdomain of internet-related activities (Table 3). This included various instruments such as the IAT [26,94], Young’s Internet Addiction Test (YIAT) [90], Korean Internet Addiction Test (K-Scale) [91], German version of the Compulsive Internet Use Scale (CIUS) [98], Generalized Problematic Internet Use Scale 2 (GPIUS2) [103], Young’s Internet Addiction Scale (YIAS) [87,93], Assessment of Internet and Computer Game Addiction-Self-report (AICA-S) [97,101], and Online Game Cognitive Addiction Scale (OGCAS) [92], which provided more detailed assessments of GD. Additionally, outcome assessment tools based on the DSM-5 diagnostic criteria were utilized in seven studies for diagnosing GD, employing tools such as the DSM-5 [95,100,102], Internet Gaming Disorder Scale (IGD-scale) [25,96], Internet Gaming Disorder Scale-short form (IGDS9-SF) [99], and Chen Internet Addiction Scale-gaming version (CIAS-G) [89].
Quality of the included studies
The risk of bias in the included studies was assessed with equal weights for all domains (Figure 2A). Four studies had a “low risk” across all domains [97,101-103]. In contrast, three had overall “high risk,” particularly in D1 and D3 [91,93,100].
A domain-specific evaluation showed that, in D1, 10 studies employed sequence generation software or random number generators for random allocation, leading to a rating of “low risk [25,89,90,92,94,97,99,101-103].” In contrast, the studies by Li et al. [91] and Park et al. [93] lacked specific information on the randomization methods and allocation concealment, with statistically significant baseline differences between intervention groups, and were classified as “high risk.” The remaining six studies lacked clarity on randomization and allocation concealment, leading to an assessment of “some concern” [87,88,95,96,98,100].
In D2, nine studies indicated that either participants or intervention providers were aware of the intervention group; however, the resulting attrition had a limited impact on the study outcomes; thus, these studies were rated as having “some concern [87,88,90-92,95,96,98,99].” Conversely, nine studies were assessed as “low risk” because neither participants nor providers were aware of the intervention group or no attrition affected the results [26,89,93,94,97,100-103].
In D3, 15 studies provided complete outcome data, resulting in a “low risk” rating [26,87-89,92-97,99-103]. In contrast, the studies by Li et al. [91] and Lindenberg et al. [98] had missing data, with insufficient evidence to confirm the absence of bias, leading to a rating of “some concern.” Notably, Nielsen et al. [100] reported IAT data only at baseline and at 5-week follow-up, with missing data for planned assessments at weeks 1 and 25, resulting in a classification of “high risk.”
In D4, 12 studies had appropriate outcome measurements with consistent methods across intervention groups, leading to a rating of “low risk [26,88,90,93,94,96-99,101-103].” However, six studies had evaluators who were aware of the intervention group and were rated as “some concern [87,89,91,92,95,100].”
In D5, 14 studies utilized data according to pre-specified analytical plans, which resulted in a “low risk” assessment [87-92,94,96-99,101-103]. Conversely, four studies lacked detailed information on multilevel analytical procedures, leading to a rating of “some concern [25,93,95,100].”
Distribution of risk of bias by domain (Figure 2B) revealed some “high risk” studies in D1 and D3. Notably, approximately 10% of studies in D1 were rated as “high risk,” while in D3, only one was classified as “high risk,” with a few others as “some concern.” In D2 and D5, although some studies had “some concern,” the majority had “low risk.”
Results of individual studies
The analysis of the effect of NPIs on GD included a total of 19 comparison-control studies. The NPI group (n=908) demonstrated a statistically significant reduction in GD symptoms compared to the control group (n=1,057) (Figure 3). The pooled effect size (Hedges’s g) was -0.82, indicating a significant effect of NPIs in reducing GD (95% CI, -1.23 to -0.52; p<0.001).
The Forest plot analysis revealed that the study by Ede et al. [103] had a notably larger effect size compared to other studies, with the remaining studies displaying a wide range of effect sizes. The heterogeneity analysis showed a high level of variability, with an I2 value of 90.36% and a Tau value of 0.653, indicating substantial heterogeneity (p<0.0001).
Synthesis of meta-analysis results
Sensitivity analysis confirmed that the pooled effect size (Hedges’s g) was stable even after sequentially removing individual studies (Figure 4). The estimated pooled effect size for GD severity ranged -0.72 (95% CI, -1.05 to -0.39) to -0.96 (95% CI, -1.37 to -0.55) after excluding each study individually, with no significant changes in the overall effect size. Notably, even when the study by Ede et al. [103], which had an outlier effect size, was excluded, the pooled effect size remained stable.
Publication bias
The funnel plot analysis revealed some asymmetry, suggesting potential publication bias (Figure 5). Most studies were clustered toward the top, indicating larger sample sizes; however, the study by Ede et al. [103] was outside the CIs, showing an outlier effect. Both Begg and Egger tests confirmed statistically significant publication bias (Begg, p=0.043; Egger, p=0.002; SE=1.189; intercept=-4.418).
The trim-and-fill method to adjust for publication bias showed a pooled effect size (Hedges’s g) of -0.46 (95% CI, -0.55 to -0.37) before adjustment and -1.25 (95% CI, -1.17 to -0.90) post-adjustment, indicating a pronounced effect.
Subgroup analysis
Subgroup analyses evaluated the variation in the effectiveness of NPIs (Table 4). The mixed-effects model analysis revealed that psychotherapy had the most significant effect, with a Hedges’s g of -1.34 (95% CI, -1.97 to -0.70), indicating a greater reduction in GD symptoms compared to behavioral interventions. In intervention goals, treatments were more effective than preventions, with a Hedges’s g of -1.13 (95% CI, -1.67 to -0.59). Additionally, the adult group showed a stronger effect compared to the adolescent group (Hedges’s g=-1.14 vs. -0.45). Notably, studies with a low risk of bias exhibited the most substantial effects (Hedges’s g=-4.08).
DISCUSSION
This study conducted a systematic review and meta-analysis to evaluate the impact of NPIs on reducing GD. A total of 18 RCTs were analyzed, which demonstrated that NPIs significantly reduced GD symptoms compared to control groups (Hedges’s g=-0.82; 95% CI, -1.23 to -0.52). Sensitivity analysis confirmed the robustness of the results, and publication bias assessments indicated that the effect sizes remained stable even after adjustment.
NPIs were effective in reducing GD in both adolescents and adults. In particular, this study demonstrated positive outcomes of psychosocial interventions such as CBT, multidimensional family therapy (MDFT), and virtual reality-based trainings (VRT). Sharma and Weinstein [104] reported that CBT is one of the most effective and well-established interventions for managing GD, while Stevens et al. [18] confirmed through a meta-analysis that CBT significantly reduced IGD symptoms (Hedges’s g=0.92; 95% CI, 0.50 to 1.34). Similarly, the me-ta-analysis by Jiang et al. [105] demonstrated that both CBT and physical activity-based interventions are effective in reducing GD symptoms. These NPIs primarily focus on correcting distorted cognitions and negative behavioral patterns related to gaming, while promoting positive alternative behaviors, reaffirming their efficacy as viable treatment options for managing GD.
This study extends previous findings by systematically comparing the effectiveness of treatment- and prevention-focused NPIs, a distinction that was not explicitly examined in earlier reviews. The findings indicate that treatment-focused NPIs are significantly more effective than prevention-focused NPIs, suggesting that existing prevention strategies may require further refinement to enhance their impact. This distinction provides critical insights into the development of targeted interventions, as prevention-focused NPIs often involve schoolbased education or parental control programs, which may not adequately address gaming-related cognitive distortions and behavioral reinforcements.
The subgroup analysis comparing the effectiveness of NPIs based on intervention goals showed that treatment interventions were significantly more effective than prevention interventions. Studies with a treatment focus primarily aimed at alleviating GD symptoms and reducing the negative impacts of excessive gaming. For instance, Nielsen et al. [100] highlighted an approach where the objective was not to completely eliminate gaming but to help adolescents reduce harmful effects and adopt healthier gaming habits. In contrast, prevention-focused studies primarily employed school-based programs and abstinence strategies to proactively manage gaming behavior. These prevention interventions mainly targeted at-risk youth, focusing on education and behavioral regulation to prevent GD development.
In the age group-based analysis, young adults showed a significantly greater reduction in GD symptoms through NPIs compared to adolescents (Hedges’s g=-1.14 vs. -0.45). This find-ing aligns with those of Stevens et al. [18], who reported higher effectiveness in adults. Adolescents often exhibit lower motivation to engage in treatment and a greater resistance to seek external help [106]. Many of them believe they do not have a problem or feel capable of managing it on their own, which limits their engagement in therapeutic interventions [107]. Additionally, the stigma associated with IGD may discourage adolescents from seeking help, thereby diminishing the effectiveness of interventions [18]. Given these differences in treatment responsiveness, identifying intervention strategies tailored to specific populations is crucial.
To address these challenges, this study incorporated a detailed subgroup analysis based on age groups, intervention goals, and control types, providing a clearer understanding of which intervention strategies are most effective for different populations. Such stratified analyses were not extensively covered in prior reviews, making this study an important addition to the literature [26,27].
The analysis of various intervention types revealed that MDFT demonstrated particularly positive outcomes in adolescents. This approach, involving family members, facilitated improvements in perceptions and behaviors related to gaming and enhanced communication within the family, thereby fostering sustainable behavioral changes [108,109]. These findings suggest that adolescents may show better treatment responsiveness when supported by their families. In contrast, digital detox and physical activity-based interventions exhibited relatively lower effectiveness. These approaches primarily focused on behavioral control but did not sufficiently address cognitive components, potentially limiting their impact on longterm changes [93]. However, VRT positively influenced engagement in real-life activities and reduced gaming behavior [110]. Specifically, it was effective in decreasing gaming time and enhancing real-life social interactions among adolescents [93]. These results indicate that a multidimensional approach, which combines psychological and behavioral components, may lead to more substantial treatment outcomes in managing GD.
This study has several limitations. First, the heterogeneity among studies was notably high (I2=90.36%), which may be attributed to differences in intervention methods, sample characteristics, and assessment tools. Second, some studies exhibited a risk of bias, potentially lowering the reliability of the results due to uncertainties in the randomization process and deviations during intervention implementation. Notably, Li et al. [91] and Park et al. [93] demonstrated a high risk of bias due to selective outcome reporting. Third, this study primarily assessed short-term effects, which limits the evaluation of the long-term sustainability of NPIs. Although some interventions, particularly psychotherapeutic approaches, demonstrate promising results, more longitudinal studies are required to determine whether these effects persist over time.
Despite these limitations, this study synthesized findings from a total of 18 RCTs, demonstrating that NPIs have a significant effect in reducing GD. Sensitivity analysis indicated that the consistency of results was maintained even after the removal of specific studies, suggesting the robustness of the effects of NPIs. Importantly, the pooled intervention effects demonstrated the potential for application across diverse clinical settings. Furthermore, this study systematically compared the effects of NPIs among both adolescents and adults, and evaluated the impact of various intervention methods on reducing GD. By distinguishing between treatment- and prevention-focused NPIs, this study provides critical insights for tailoring interventions to specific populations. In particular, psychosocial interventions such as CBT, MDFT, and VRT were effective in reducing GD symptoms and improving treatment outcomes, highlighting their potential for future clinical applications.
Future research should focus on long-term follow-up to evaluate the sustained effects of NPIs and explore their applicability across diverse population. Additionally, the use of standardized assessment tools and consistent intervention protocols is necessary to reduce heterogeneity across studies and minimize bias. Large-scale, high-quality RCTs with pre-registered designs are needed to further validate these findings. Such efforts would facilitate the development of more effective strategies to reduce and prevent GD.
In conclusion, the findings highlight the promising effect of NPIs—particularly CBT-based interventions—in reducing GD symptoms. However, the current evidence is limited by the small number of high-quality studies available. To enhance the evidence base, future studies should prioritize well-designed, large-scale RCTs with pre-registered protocols, standardized outcome measures. Such efforts will be crucial in establishing robust, evidence-based treatment guidelines for GD.
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